Power grid data connection and matching method based on multi-source data

Abstract

The invention discloses a power grid data connection and matching method based on multi-source data, relates to the technical field of smart power grids, and solves the problems that different data are difficult to collect and process through nodes of different levels, the data are difficult to manage and analyze, the data are inconvenient to longitudinally manage through a data table, and meanwhile, the data are difficult to process. And the technical problem of transverse query of data is solved. By collecting topological data, monitoring data, metering data and geographic information data and adding labels to the topological data, the monitoring data, the metering data and the geographic information data, integration of multi-source data is achieved, high-level nodes are provided with edge computing task agent nodes and data warehouses, and the transformer area power grid data can be conveniently dispersed to all the high-level nodes to be processed and analyzed. By constructing the similarity evaluation model between the low-level nodes, the nodes with similar functions, performance or features can be identified more accurately, the association link between the node topology data between the similar nodes is established, and the data intercommunication and sharing between the similar nodes are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart grid, and in particular is a method for connecting and matching grid data based on multi-source data. Background Art

[0002] With the rapid development of smart grid technology, the operation and management of power systems have become increasingly complex. There are many types of data sources in the power grid, including but not limited to: data from sensors, smart meters, protection devices and other equipment, as well as structural information of the power grid, including the connection relationship between nodes, lines, transformers and other equipment. Therefore, it is necessary to effectively integrate and match data from different sources to achieve comprehensive monitoring and analysis of the power grid status. The power grid data connection and matching method based on multi-source data needs to collect, integrate and process data from different sources to achieve a comprehensive understanding and analysis of the power grid status.

[0003] Most power grid data connection and matching solutions only standardize the data, making it difficult to collect and process different data through nodes at different levels and manage and analyze the data. It is also not convenient to manage the data vertically through data tables, and at the same time, facilitate horizontal query of the data. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a power grid data connection and matching method based on multi-source data, which is used to solve the technical problems that it is difficult to collect and process different data through nodes at different levels, and to manage and analyze the data, and it is not convenient to manage the data vertically through data tables, and at the same time, it is convenient for horizontal query of data.

[0005] To solve the above problems, a first aspect of the present invention provides a power grid data connection and matching method based on multi-source data, comprising the following steps:

[0006] Obtain the topological structure data of the power grid in the substation area, and collect the topological data, monitoring data, metering data and geographic information data of each node, and add data labels to the collected data;

[0007] According to the topological structure data of the substation power grid, as well as the monitoring data and metering data of the nodes, the nodes are divided into high-level nodes, middle-level nodes and low-level nodes. Edge computing task agent nodes and data warehouses are set up at high-level nodes, and data warehouses are set up at middle-level nodes.

[0008] According to the topological data and monitoring data of the nodes, the middle-level nodes and the low-level nodes are grouped into different high-level node management groups, a communication channel to the middle-level nodes is established through the low-level nodes in each high-level node management group, and the data of the low-level nodes and the current node are stored in the data warehouse. A communication channel to the high-level nodes is established through the middle-level nodes in each high-level node management group, and the data of the middle-level nodes and the current node are stored in the data warehouse;

[0009] The edge computing task proxy node of the high-level node standardizes the data collected by the node and establishes links between the data collected by the node;

[0010] Based on the topological data, monitoring data, metering data and geographic information data of low-level nodes, a similarity evaluation model between low-level nodes is constructed. The similarity between each node is evaluated through the similarity evaluation model between low-level nodes, and the association links between the node topological data of high-similarity nodes are established.

[0011] As a further solution of the present invention: obtaining the topological structure data of the power grid in the substation area, collecting the topological data, monitoring data, metering data and geographic information data of each node, and adding data tags to the collected data, including the following steps:

[0012] Obtain the authorized area power grid topology data, and collect the topology data, monitoring data, metering data and geographic information data of each node;

[0013] Among them, topological data includes node degree, node edge weight and proximity centrality; monitoring data includes SCADA data and equipment failure data; metering data includes smart meter data and DAS distribution automation system data; geographic information data includes GIS data and node meteorological data;

[0014] The nodes in the topological structure of the substation power grid are numbered, and a unique device ID is added to each device in the node. Data tags are added to the data collected by the node. The data tags include: timestamp, device ID and node number.

[0015] As a further solution of the present invention: according to the topological structure data of the power grid in the substation area, as well as the monitoring data and metering data of the nodes, the nodes are divided into high-level nodes, middle-level nodes and low-level nodes, including the following steps:

[0016] S1: The topological structure data of the substation power grid, as well as the monitoring data and metering data of the nodes are standardized by the Z-score standardization method;

[0017] S2: The standardized data are organized into data groups according to the order of node degree, node edge weight and closeness centrality, SCADA data and equipment failure data, smart meter data and DAS distribution automation system data;

[0018] S3: Using the K-means clustering algorithm, the goal is to divide the nodes into three levels: high, medium, and low, and set the number of clusters k=3;

[0019] S4: Randomly select k nodes as initial cluster centers;

[0020] S5: Calculate the Euclidean distance between the data group of each node and the data group of the cluster center, and assign each node to the cluster center of the Euclidean distance;

[0021] S6: Calculate the ratio of the mean of the Euclidean distance between the data group of the nodes in the cluster and the data group of the cluster center to the mean of the data in the data groups of all the nodes in the cluster as the offset distance ratio, and reselect the center of each cluster;

[0022] S7: Repeat steps S5 and S6 until the offset distance ratio reaches a preset threshold.

[0023] As a further solution of the present invention: according to the topological data and monitoring data of the nodes, the middle-level nodes and the low-level nodes are grouped into different high-level node management groups, including the following steps:

[0024] According to the topological data and monitoring data of the nodes, the management group of each high-level node sets the threshold of the number of middle-level nodes and low-level nodes. The middle-level nodes are grouped into the high-level node management group based on the principle of the closest distance. If the number of middle-level nodes in the corresponding high-level node management group reaches the threshold, the middle-level nodes are grouped again until all the middle-level nodes are grouped; according to the same grouping principle of the middle-level nodes, the low-level nodes are grouped into different high-level node management groups;

[0025] As a further solution of the present invention: establishing a communication channel to a middle-level node through a low-level node in each high-level node management group includes the following steps:

[0026] The middle-level nodes in each high-level node management group are used as the hub of its communication channel. A threshold value for the number of low-level nodes connected to the middle-level nodes is set. Based on the principle of the closest distance, the low-level nodes in the group are connected to the middle-level nodes.

[0027] If the number of low-level nodes connected to the middle-level node reaches the threshold, the communication channel of the low-level nodes is re-established until all the low-level nodes have completed the establishment of communication channels with the middle-level nodes;

[0028] Among them, a communication channel is established through the TCP communication protocol, and the data received and collected in the middle-level nodes are converted into the same data format for storage.

[0029] As a further solution of the present invention: the edge computing task proxy node of the high-level node standardizes the data collected by the node and establishes a link between the node collected data, including the following steps:

[0030] Establish a library of data standardization rules to convert the collected data into the same format and naming conventions;

[0031] Set up edge computing task agent nodes at high-level nodes to establish data lists of monitoring data, metering data, and geographic information data, and establish mapping relationships between various data lists based on data labels of node data;

[0032] According to the mapping relationship between the data lists, the key-value pairs of the topological data of the node and the data in the corresponding data list are constructed, the primary key is assigned to the topological data of the node, and the foreign key is assigned to the corresponding data in the data list.

[0033] As a further solution of the present invention: an edge computing task proxy node is set at a high-level node to establish a data list of monitoring data, metering data and geographic information data, and a mapping relationship between each data list is established according to the data label of the node data, including the following steps:

[0034] Set up edge computing task agent nodes at high-level nodes to establish data lists of monitoring data, metering data, and geographic information data, and regularly detect the creation time of each data list, transfer the number of lists whose creation time exceeds the threshold to the data warehouse for storage, and replace the specific data in the data list with the storage address;

[0035] According to the data label of the node data, create an index for each type of data. The index is: device ID + node number + timestamp;

[0036] The mapping relationship between each data list is established as follows:

[0037] Traverse each record in the data lists of monitoring data, metering data, and geographic information data, and find out whether there is a record corresponding to the index in each data list according to the data label of the data in the list;

[0038] If it exists, the found data are combined together to form a new comprehensive record;

[0039] All combined records are stored in a new list to form a dataset for query data, including monitoring, measurement and geographic information.

[0040] As a further solution of the present invention: constructing a similarity evaluation model between low-level nodes based on topological data, monitoring data, metering data and geographic information data of low-level nodes, including the following steps:

[0041] According to the topological data of the low-level nodes, the coincidence similarity between nodes is evaluated using the following formula:

[0042]

[0043] Among them, Sto(A,B) is the coincidence similarity between nodes, N(A) is the set of neighbor nodes connected to nodes A and B, and N(B) is the set of neighbor nodes connected to node B;

[0044] According to the monitoring data and metering data of the low-level nodes, the monitoring data and metering data are combined into a feature data set, which contains a total of n feature data. The data similarity between nodes is evaluated by the following formula:

[0045]

[0046]

[0047] Among them, Smo(A,B) is the data similarity between nodes, Dmo(A,B) is the Euclidean distance of the i-th feature data in the feature data sets of nodes A and B, XAi is the value of the i-th feature data in the feature data set of node A, and XBi is the value of the i-th feature data in the feature data set of node B;

[0048] According to the geographic information data of the low-level nodes, the location similarity between nodes is evaluated using the following formula:

[0049]

[0050] Among them, Sgo(A,B) is the position similarity between nodes, d(A,B) is the distance between nodes A and B, and μ(A,B) is the mean distance between all nodes in the group where nodes A and B are located;

[0051] According to the overlap similarity between low-level nodes, the data similarity between nodes, and the position similarity between nodes, a similarity evaluation model between low-level nodes is constructed using the following formula:

[0052]

[0053] Among them, S(A, B) is the similarity evaluation value between low-level nodes, w1, w2 and w3 are the importance weights of the coincidence similarity between nodes, the data similarity between nodes and the position similarity between nodes, respectively.

[0054] As a further solution of the present invention: the similarity between each node is evaluated by a similarity evaluation model between low-level nodes, and an association link between node topology data of high-similarity nodes is established, including the following steps:

[0055] The similarity between each node is evaluated through the similarity evaluation model between low-level nodes. If the similarity evaluation value between nodes is greater than the threshold, the node is judged to be a high-similarity node, otherwise, the node is a low-similarity node;

[0056] Establishing association links between node topology data of highly similar nodes;

[0057] Detect the middle-level nodes where the topological data of nodes between high-similarity nodes are stored. If the data of two high-similarity nodes are stored in different middle-level nodes, query the foreign keys of the data according to the data labels of the other nodes respectively. According to the primary keys corresponding to the queried foreign keys, query the topological data of the lower-level nodes of the corresponding nodes, establish the communication links between the middle-level nodes where the node topological data of the high-similarity nodes are stored, and associate the node topological data of the high-similarity nodes.

[0058] If the data of two highly similar nodes are stored in the same middle-level node, the two highly similar nodes will query the foreign keys of the data based on the data labels of the other nodes, and query the topological data of the corresponding low-level nodes based on the primary keys corresponding to the queried foreign keys, and associate the node topological data of the highly similar nodes.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The present invention collects topological data, monitoring data, metering data and geographic information data, and adds labels to them to achieve the integration of multi-source data, ensure that data from different sources are analyzed under the same framework, and improve the consistency and availability of data. High-level nodes set edge computing task proxy nodes and data warehouses to facilitate the data processing and analysis of the power grid data in the substation area distributed to each high-level node, and improve the efficiency and security of the operation of power grid data analysis.

[0061] The present invention ensures the consistency of data from different sources and formats by standardizing the collected data, making the subsequent data analysis more accurate and reliable. At the same time, by constructing a similarity evaluation model between low-level nodes, it is convenient to more accurately identify nodes with similar functions, performances or characteristics, establish an association link between the node topology data between similar nodes, realize the intercommunication and sharing of data between similar nodes, and also facilitate the subsequent unified analysis of similar node data, providing support for subsequent data analysis and decision-making.

[0062] The present invention also helps optimize resource allocation by establishing association links between high-similarity nodes, can quickly locate problems when a fault occurs, and draw on data from other normal nodes to perform fault analysis and recovery, thereby improving the reliability of the power grid. At the same time, it facilitates subsequent collaborative analysis between high-similarity nodes, improving the flexibility and response speed of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0065] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] See also Figure 1 The first embodiment of the present invention provides a method for connecting and matching power grid data based on multi-source data, comprising the following steps:

[0067] Obtain the topological structure data of the power grid in the substation area, and collect the topological data, monitoring data, metering data and geographic information data of each node, and add data labels to the collected data;

[0068] According to the topological structure data of the substation power grid, as well as the monitoring data and metering data of the nodes, the nodes are divided into high-level nodes, middle-level nodes and low-level nodes. Edge computing task agent nodes and data warehouses are set up at high-level nodes, and data warehouses are set up at middle-level nodes.

[0069] According to the topological data and monitoring data of the nodes, the middle-level nodes and the low-level nodes are grouped into different high-level node management groups, a communication channel to the middle-level nodes is established through the low-level nodes in each high-level node management group, and the data of the low-level nodes and the current node are stored in the data warehouse. A communication channel to the high-level nodes is established through the middle-level nodes in each high-level node management group, and the data of the middle-level nodes and the current node are stored in the data warehouse;

[0070] The edge computing task proxy node of the high-level node standardizes the data collected by the node and establishes links between the data collected by the node;

[0071] Based on the topological data, monitoring data, metering data and geographic information data of low-level nodes, a similarity evaluation model between low-level nodes is constructed. The similarity between each node is evaluated through the similarity evaluation model between low-level nodes, and the association links between the node topological data of high-similarity nodes are established.

[0072] Specifically, in this embodiment, the topological structure data of the power grid in the substation area is obtained, and the topological data, monitoring data, metering data and geographic information data of each node are collected, and data labels are added to the collected data; according to the topological structure data of the power grid in the substation area, and the monitoring data and metering data of the nodes, the nodes are divided into high-level nodes, middle-level nodes and low-level nodes, and edge computing task agent nodes and data warehouses are set at high-level nodes, and data warehouses are set at middle-level nodes; by collecting topological data, monitoring data, metering data and geographic information data, and adding labels to them, the integration of multi-source data is realized, ensuring that data from different sources are analyzed under the same framework, and improving the consistency and availability of data. The high-level nodes are set with edge computing task agent nodes and data warehouses, which facilitates the data processing and analysis of the power grid data in the substation area to be distributed to each high-level node for processing, and improves the efficiency and security of the operation of power grid data analysis. Dividing nodes into high, medium and low levels helps to establish a hierarchical management mechanism. High-level nodes are responsible for global coordination and decision-making, middle-level nodes are responsible for data storage and processing, and low-level nodes focus on specific monitoring and metering work. This hierarchical structure can optimize resource allocation and improve system response speed. It is also convenient to analyze the real-time monitoring data of each node, discover potential problems in time, reduce the probability of failure, and improve the reliability of power grid operation. Through the data warehouse setting of edge computing and middle-level nodes, the data of low-level nodes are aggregated and integrated through middle-level nodes, which makes it easy to flexibly adjust task allocation and resource use according to actual needs and improve the adaptability of the system.

[0073] According to the topological data and monitoring data of the nodes, the middle-level nodes and the low-level nodes are grouped into different high-level node management groups, a communication channel to the middle-level nodes is established through the low-level nodes in each high-level node management group, and the data of the low-level nodes and the current node are stored in the data warehouse. A communication channel to the high-level nodes is established through the middle-level nodes in each high-level node management group, and the data of the middle-level nodes and the current node are stored in the data warehouse;

[0074] By establishing communication channels from low-level nodes to middle-level nodes, and from middle-level nodes to high-level nodes, it is convenient to achieve fast and stable data transmission between low-level nodes and middle-level nodes, reduce delays, increase data update frequency, and also facilitate data transmission to high-level nodes.

[0075] Grouping middle-level and low-level nodes into different high-level node management helps achieve distributed management, improve the scalability and flexibility of the system, and facilitate future expansion needs. By monitoring the data of low-level nodes in real time and reporting to middle-level and high-level nodes, problems can be quickly identified and responded to, improving the reliability and safety of power grid operation.

[0076] The edge computing task proxy node of the high-level node standardizes the data collected by the node and establishes links between the data collected by the nodes; based on the topological data, monitoring data, metering data and geographic information data of the low-level nodes, a similarity evaluation model between low-level nodes is constructed, and the similarity between each node is evaluated through the similarity evaluation model between low-level nodes, and the association link between the node topological data of high-similarity nodes is established;

[0077] By standardizing the collected data, we ensure that data from different sources and formats are consistent, making subsequent data analysis more accurate and reliable. At the same time, by building a similarity evaluation model between low-level nodes, we can more accurately identify nodes with similar functions, performance or characteristics, establish association links between node topology data between similar nodes, achieve intercommunication and sharing of data between similar nodes, and facilitate subsequent unified analysis of similar node data, providing support for subsequent data analysis and decision-making.

[0078] By establishing association links between highly similar nodes, it is also helpful to optimize resource allocation, quickly locate the problem when a fault occurs, and draw on the data of other normal nodes for fault analysis and recovery, thereby improving the reliability of the power grid. At the same time, it is convenient for subsequent collaborative analysis between highly similar nodes, improving the flexibility and response speed of the overall system.

[0079] In one embodiment of the present invention, obtaining the topological structure data of the power grid in the substation area, collecting the topological data, monitoring data, metering data and geographic information data of each node, and adding data tags to the collected data, includes the following steps:

[0080] Obtain the authorized area power grid topology data, and collect the topology data, monitoring data, metering data and geographic information data of each node;

[0081] Among them, topological data includes node degree, node edge weight and proximity centrality; monitoring data includes SCADA data and equipment failure data; metering data includes smart meter data and DAS distribution automation system data; geographic information data includes GIS data and node meteorological data;

[0082] The nodes in the topological structure of the substation power grid are numbered, and a unique device ID is added to each device in the node. Data tags are added to the data collected by the node. The data tags include: timestamp, device ID and node number.

[0083] Specifically, in this embodiment, the node number is set in the following manner:

[0084] According to the topology of the power grid in the substation area, a unique number is assigned to each node. For example, suppose there are 50 nodes, numbered Node_001, Node_002, ..., Node_050.

[0085] Device ID Assignment:

[0086] For each device in a node, a unique device ID is assigned. The device ID can be generated using the format of "node number + device serial number". For example:

[0087] The devices in Node_001 can be marked as Device_001-01, Device_001-02;

[0088] The devices in Node_002 can be labeled Device_002-01, Device_002-02, and so on.

[0089] When collecting data, add corresponding data tags to each data record, including timestamp, device ID and node number.

[0090] The timestamp can be in a standard format such as ISO 8601, for example "2023-10-01T12:00:00Z".

[0091] Data label example:

[0092] timestamp:2023-10-01T12:00:00Z,device_id:Device_001-01,node_id:Node_001.

[0093] In one embodiment of the present invention, according to the topological structure data of the power grid in the substation area, and the monitoring data and metering data of the nodes, the nodes are divided into high-level nodes, middle-level nodes and low-level nodes, including the following steps:

[0094] S1: The topological structure data of the substation power grid, as well as the monitoring data and metering data of the nodes are standardized by the Z-score standardization method;

[0095] S2: The standardized data are organized into data groups according to the order of node degree, node edge weight and closeness centrality, SCADA data and equipment failure data, smart meter data and DAS distribution automation system data;

[0096] S3: Using the K-means clustering algorithm, the goal is to divide the nodes into three levels: high, medium, and low, and set the number of clusters k=3;

[0097] S4: Randomly select k nodes as initial cluster centers;

[0098] S5: Calculate the Euclidean distance between the data group of each node and the data group of the cluster center, and assign each node to the cluster center of the Euclidean distance;

[0099] S6: Calculate the ratio of the mean of the Euclidean distance between the data group of the nodes in the cluster and the data group of the cluster center to the mean of the data in the data groups of all the nodes in the cluster as the offset distance ratio, and reselect the center of each cluster;

[0100] S7: Repeat steps S5 and S6 until the offset distance ratio reaches a preset threshold.

[0101] In this embodiment, relevant data is collected, including:

[0102] Node degree: The number of connections of each node, reflecting its importance in the network.

[0103] Edge weight: The weight of each edge, indicating the importance of the connection, such as current capacity, impedance, etc.

[0104] Closeness centrality: Calculates the average distance between each node and other nodes, reflecting its centrality in the network.

[0105] SCADA data: real-time monitoring data such as current, voltage, power, etc.

[0106] Equipment failure data: historical failure records, including failure frequency and impact scope.

[0107] Smart meter data: information on electricity demand and usage patterns.

[0108] DAS data: Status information in the distribution automation system, such as switch status and fault detection.

[0109] In one embodiment of the present invention, according to the topological data and monitoring data of the nodes, the middle-level nodes and the low-level nodes are grouped into different high-level node management groups, including the following steps:

[0110] According to the topological data and monitoring data of the nodes, the management group of each high-level node sets the threshold of the number of middle-level nodes and low-level nodes. The middle-level nodes are grouped into the high-level node management group based on the principle of the closest distance. If the number of middle-level nodes in the corresponding high-level node management group reaches the threshold, the middle-level nodes are grouped again until all the middle-level nodes are grouped; according to the same grouping principle of the middle-level nodes, the low-level nodes are grouped into different high-level node management groups;

[0111] In one embodiment of the present invention, establishing a communication channel to a middle-level node through a low-level node in each high-level node management group includes the following steps:

[0112] The middle-level nodes in each high-level node management group are used as the hub of its communication channel. A threshold value for the number of low-level nodes connected to the middle-level nodes is set. Based on the principle of the closest distance, the low-level nodes in the group are connected to the middle-level nodes.

[0113] If the number of low-level nodes connected to the middle-level node reaches the threshold, the communication channel of the low-level nodes is re-established until all the low-level nodes have completed the establishment of communication channels with the middle-level nodes;

[0114] Among them, a communication channel is established through the TCP communication protocol, and the data received and collected in the middle-level nodes are converted into the same data format for storage.

[0115] In one embodiment of the present invention, the edge computing task proxy node of the high-level node standardizes the data collected by the node and establishes a link between the node collected data, including the following steps:

[0116] Establish a library of data standardization rules to convert the collected data into the same format and naming conventions;

[0117] Set up edge computing task agent nodes at high-level nodes to establish data lists of monitoring data, metering data, and geographic information data, and establish mapping relationships between various data lists based on data labels of node data;

[0118] According to the mapping relationship between the data lists, the key-value pairs of the topological data of the node and the data in the corresponding data list are constructed, the primary key is assigned to the topological data of the node, and the foreign key is assigned to the corresponding data in the data list.

[0119] In one embodiment of the present invention, an edge computing task proxy node is set at a high-level node to establish a data list of monitoring data, metering data, and geographic information data, and a mapping relationship between each data list is established according to the data label of the node data, including the following steps:

[0120] Set up edge computing task agent nodes at high-level nodes to establish data lists of monitoring data, metering data, and geographic information data, and regularly detect the creation time of each data list, transfer the number of lists whose creation time exceeds the threshold to the data warehouse for storage, and replace the specific data in the data list with the storage address;

[0121] According to the data label of the node data, create an index for each type of data. The index is: device ID + node number + timestamp;

[0122] The mapping relationship between each data list is established as follows:

[0123] Traverse each record in the data lists of monitoring data, metering data, and geographic information data, and find out whether there is a record corresponding to the index in each data list according to the data label of the data in the list;

[0124] If it exists, the found data are combined together to form a new comprehensive record;

[0125] All combined records are stored in a new list to form a dataset for query data, including monitoring, measurement and geographic information.

[0126] In one embodiment of the present invention, a similarity evaluation model between low-level nodes is constructed based on topological data, monitoring data, metering data and geographic information data of low-level nodes, including the following steps:

[0127] Based on the topological data of the low-level nodes, the coincidence similarity between nodes is evaluated using the following formula:

[0128]

[0129] Among them, Sto(A,B) is the coincidence similarity between nodes, N(A) is the set of neighbor nodes connected to nodes A and B, and N(B) is the set of neighbor nodes connected to node B;

[0130] According to the monitoring data and metering data of the low-level nodes, the monitoring data and metering data are combined into a feature data set, which contains a total of n feature data. The data similarity between nodes is evaluated by the following formula:

[0131]

[0132]

[0133] Among them, Smo(A,B) is the data similarity between nodes, Dmo(A,B) is the Euclidean distance of the i-th feature data in the feature data sets of nodes A and B, XAi is the value of the i-th feature data in the feature data set of node A, and XBi is the value of the i-th feature data in the feature data set of node B;

[0134] According to the geographic information data of the low-level nodes, the location similarity between nodes is evaluated using the following formula:

[0135]

[0136] Among them, Sgo(A,B) is the position similarity between nodes, d(A,B) is the distance between nodes A and B, and μ(A,B) is the mean distance between all nodes in the group where nodes A and B are located;

[0137] According to the overlap similarity between low-level nodes, the data similarity between nodes, and the position similarity between nodes, a similarity evaluation model between low-level nodes is constructed using the following formula:

[0138]

[0139] Among them, S(A, B) is the similarity evaluation value between low-level nodes, w1, w2 and w3 are the importance weights of the coincidence similarity between nodes, the data similarity between nodes and the position similarity between nodes, respectively.

[0140] Specifically, in this embodiment, based on the calculation and evaluation of the similarity evaluation values ​​between a large number of low-level nodes, the overlap similarity importance weight w1 is set to 0.2, the data similarity importance weight w2 between nodes is set to 0.6, and the importance weight w3 between nodes is set to 0.2.

[0141] In one embodiment of the present invention, the similarity between each node is evaluated by a similarity evaluation model between low-level nodes, and an association link between node topology data of high-similarity nodes is established, including the following steps:

[0142] The similarity between each node is evaluated through the similarity evaluation model between low-level nodes. If the similarity evaluation value between nodes is greater than the threshold, the node is judged to be a high-similarity node, otherwise, the node is a low-similarity node;

[0143] Establishing association links between node topology data of highly similar nodes;

[0144] Detect the middle-level nodes where the topological data of nodes between high-similarity nodes are stored. If the data of two high-similarity nodes are stored in different middle-level nodes, query the foreign keys of the data according to the data labels of the other nodes respectively. According to the primary keys corresponding to the queried foreign keys, query the topological data of the lower-level nodes of the corresponding nodes, establish the communication links between the middle-level nodes where the node topological data of the high-similarity nodes are stored, and associate the node topological data of the high-similarity nodes.

[0145] If the data of two highly similar nodes are stored in the same middle-level node, the two highly similar nodes will query the foreign keys of the data based on the data labels of the other nodes, and query the topological data of the corresponding low-level nodes based on the primary keys corresponding to the queried foreign keys, and associate the node topological data of the highly similar nodes.

[0146] Specifically, in this embodiment, the similarity between each node is evaluated by a similarity evaluation model between low-level nodes. If the similarity evaluation value between nodes is greater than 0.5, the node is judged to be a high-similarity node, otherwise, the node is a low-similarity node.

[0147] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A power grid data connection and matching method based on multi-source data, characterized in that: The following steps are involved: Obtain the topological structure data of the power grid in the substation area, and collect the topological data, monitoring data, metering data and geographic information data of each node, and add data labels to the collected data; According to the topological structure data of the substation power grid, as well as the monitoring data and metering data of the nodes, the nodes are divided into high-level nodes, middle-level nodes and low-level nodes. Edge computing task agent nodes and data warehouses are set up at high-level nodes, and data warehouses are set up at middle-level nodes. According to the topological data and monitoring data of the nodes, the middle-level nodes and the low-level nodes are grouped into different high-level node management groups, a communication channel to the middle-level nodes is established through the low-level nodes in each high-level node management group, and the data of the low-level nodes and the current node are stored in the data warehouse. A communication channel to the high-level nodes is established through the middle-level nodes in each high-level node management group, and the data of the middle-level nodes and the current node are stored in the data warehouse; The edge computing task proxy node of the high-level node standardizes the data collected by the node and establishes links between the data collected by the node; Based on the topological data, monitoring data, metering data and geographic information data of low-level nodes, a similarity evaluation model between low-level nodes is constructed. The similarity between each node is evaluated through the similarity evaluation model between low-level nodes, and the association links between the node topological data of high-similarity nodes are established.

2. The power grid data connection and matching method based on multi-source data according to claim 1 is characterized in that: Obtaining the topological structure data of the power grid in the substation area, collecting the topological data, monitoring data, metering data and geographic information data of each node, and adding data labels to the collected data, including the following steps: Obtain the authorized area power grid topology data, and collect the topology data, monitoring data, metering data and geographic information data of each node; Among them, topological data includes node degree, node edge weight and proximity centrality; monitoring data includes SCADA data and equipment failure data; metering data includes smart meter data and DAS distribution automation system data; geographic information data includes GIS data and node meteorological data; The nodes in the topological structure of the substation power grid are numbered, and a unique device ID is added to each device in the node. Data tags are added to the data collected by the node. The data tags include: timestamp, device ID and node number.

3. The power grid data connection and matching method based on multi-source data according to claim 1 is characterized in that: According to the topological structure data of the power grid in the substation area, as well as the monitoring data and metering data of the nodes, the nodes are divided into high-level nodes, middle-level nodes and low-level nodes, including the following steps: S1: The topological structure data of the substation power grid, as well as the monitoring data and metering data of the nodes are standardized by the Z-score standardization method; S2: The standardized data are organized into data groups according to the order of node degree, node edge weight and closeness centrality, SCADA data and equipment failure data, smart meter data and DAS distribution automation system data; S3: Using the K-means clustering algorithm, the goal is to divide the nodes into three levels: high, medium, and low, and set the number of clusters k=3; S4: Randomly select k nodes as initial cluster centers; S5: Calculate the Euclidean distance between the data group of each node and the data group of the cluster center, and assign each node to the cluster center of the Euclidean distance; S6: Calculate the ratio of the mean of the Euclidean distance between the data group of the nodes in the cluster and the data group of the cluster center to the mean of the data in the data groups of all the nodes in the cluster as the offset distance ratio, and reselect the center of each cluster; S7: Repeat steps S5 and S6 until the offset distance ratio reaches a preset threshold.

4. The power grid data connection and matching method based on multi-source data according to claim 1, characterized in that: According to the topological data and monitoring data of the nodes, the middle-level nodes and the low-level nodes are grouped into different high-level node management groups, including the following steps: According to the topological data and monitoring data of the nodes, the management grouping of each high-level node sets the threshold for the number of middle-level nodes and low-level nodes. The middle-level nodes are grouped into the high-level node management grouping based on the principle of the closest distance. If the number of middle-level nodes in the management grouping of the corresponding high-level node reaches the threshold, the middle-level nodes are grouped again until all the middle-level nodes are grouped; according to the same grouping principle for the middle-level nodes, the low-level nodes are grouped into different high-level node management groups.

5. The power grid data connection and matching method based on multi-source data according to claim 1 is characterized in that: Establishing a communication channel to a middle-level node through a low-level node in each high-level node management group includes the following steps: The middle-level nodes in each high-level node management group are used as the hub of its communication channel. A threshold value for the number of low-level nodes connected to the middle-level nodes is set. Based on the principle of the closest distance, the low-level nodes in the group are connected to the middle-level nodes. If the number of low-level nodes connected to the middle-level node reaches the threshold, the communication channel of the low-level nodes is re-established until all the low-level nodes have completed the establishment of communication channels with the middle-level nodes; Among them, a communication channel is established through the TCP communication protocol, and the data received and collected in the middle-level nodes are converted into the same data format for storage.

6. The method for connecting and matching power grid data based on multi-source data according to claim 1, characterized in that: The edge computing task proxy node of the high-level node standardizes the data collected by the node and establishes a link between the node collected data, including the following steps: Establish a library of data standardization rules to convert the collected data into the same format and naming conventions; Set up edge computing task agent nodes at high-level nodes to establish data lists of monitoring data, metering data, and geographic information data, and establish mapping relationships between various data lists based on data labels of node data; According to the mapping relationship between the data lists, the key-value pairs of the topological data of the node and the data in the corresponding data list are constructed, the primary key is assigned to the topological data of the node, and the foreign key is assigned to the corresponding data in the data list.

7. The method for connecting and matching power grid data based on multi-source data according to claim 6, characterized in that: Setting edge computing task agent nodes at high-level nodes to establish data lists of monitoring data, metering data, and geographic information data, and establishing mapping relationships between various data lists based on data labels of node data, including the following steps: Set up edge computing task agent nodes at high-level nodes to establish data lists of monitoring data, metering data, and geographic information data, and regularly detect the creation time of each data list, transfer the number of lists whose creation time exceeds the threshold to the data warehouse for storage, and replace the specific data in the data list with the storage address; According to the data label of the node data, create an index for each type of data. The index is: device ID + node number + timestamp; The mapping relationship between each data list is established as follows: Traverse each record in the data lists of monitoring data, metering data, and geographic information data, and find out whether there is a record corresponding to the index in each data list according to the data label of the data in the list; If it exists, the found data are combined together to form a new comprehensive record; All combined records are stored in a new list to form a dataset for query data, including monitoring, measurement and geographic information.

8. The power grid data connection and matching method based on multi-source data according to claim 1 is characterized in that: Based on the topological data, monitoring data, metering data and geographic information data of the low-level nodes, a similarity evaluation model between low-level nodes is constructed, including the following steps: According to the topological data of the low-level nodes, the coincidence similarity between nodes is evaluated using the following formula: ； Among them, Sto(A,B) is the coincidence similarity between nodes, N(A) is the set of neighbor nodes connected to nodes A and B, and N(B) is the set of neighbor nodes connected to node B; According to the monitoring data and metering data of the low-level nodes, the monitoring data and metering data are combined into a feature data set, which contains a total of n feature data. The data similarity between nodes is evaluated by the following formula: ； ； Among them, Smo(A,B) is the data similarity between nodes, Dmo(A,B) is the Euclidean distance of the i-th feature data in the feature data sets of nodes A and B, XAi is the value of the i-th feature data in the feature data set of node A, and XBi is the value of the i-th feature data in the feature data set of node B; According to the geographic information data of the low-level nodes, the location similarity between nodes is evaluated using the following formula: ； Among them, Sgo(A,B) is the position similarity between nodes, d(A,B) is the distance between nodes A and B, and μ(A,B) is the mean distance between all nodes in the group where nodes A and B are located; According to the overlap similarity between low-level nodes, the data similarity between nodes, and the position similarity between nodes, a similarity evaluation model between low-level nodes is constructed using the following formula: ； Among them, S(A, B) is the similarity evaluation value between low-level nodes, w1, w2 and w3 are the importance weights of the coincidence similarity between nodes, the data similarity between nodes and the position similarity between nodes, respectively.

9. The power grid data connection and matching method based on multi-source data according to claim 8, characterized in that: The similarity between each node is evaluated by a similarity evaluation model between low-level nodes, and an association link between node topology data of high-similarity nodes is established, including the following steps: The similarity between each node is evaluated through the similarity evaluation model between low-level nodes. If the similarity evaluation value between nodes is greater than the threshold, the node is judged to be a high-similarity node, otherwise, the node is a low-similarity node; Establishing association links between node topology data of highly similar nodes; Detect the middle-level nodes where the topological data of nodes between high-similarity nodes are stored. If the data of two high-similarity nodes are stored in different middle-level nodes, query the foreign keys of the data according to the data labels of the other nodes respectively. According to the primary keys corresponding to the queried foreign keys, query the topological data of the lower-level nodes of the corresponding nodes, establish the communication links between the middle-level nodes where the node topological data of the high-similarity nodes are stored, and associate the node topological data of the high-similarity nodes. If the data of two highly similar nodes are stored in the same middle-level node, the two highly similar nodes will query the foreign keys of the data based on the data labels of the other nodes, and query the topological data of the corresponding low-level nodes based on the primary keys corresponding to the queried foreign keys, and associate the node topological data of the highly similar nodes.

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